Applied Econometrics - Content Outline

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1. Getting Started
   • The Model Specification Phase
     • The Principal of Parsimony
     • The Shrinkage Principle
   • The Generic Single-Equation Linear Regression Model
     • Components of the Model
       • Endogenous Variables, Exogenous Variables, Lagged Exogenous Variables, Lagged Endogenous Variables
       • The Disturbance (or Error) Term
       • Assumptions of Econometric Models
   • Data and Data Transformations
     • Data Types--Time-Series, Cross-Sectional, Combination
     • Getting a Feel for the Data--Plots of Key Variables,Scatter Plots, and Descriptive Statistics
     • Massaging the Data
       • Expression of Data--Nominal versus Real or Inflation-Adjusted Measures
       • Expression of Data--Total or Per Capita Terms
       • Expression of Data--Levels, Changes, or Percentage Changes
       • Use of Moving Averages or Exponential Smoothing
       • Adjustments for Seasonality
       • Imputations for Missing Data

          

2. Mathematical and Statistical Considerations
   • Key Constructs in Applied Econometric Models
   • Estimation of Structural Parameters in Applied Econometrics
     • Marginal Effects, Standardized Regression Coefficients. Elasticities, and Partial Correlation Coefficients
   • Interval Estimation and the Construction of Confidence Intervals

      

3. Common Tests of Hypotheses
   • Tests of Hypotheses Regarding Structural Parameters of Econometric Models
     • Statistical Distributions--Standard Normal, t, Chi-squared, and F
     • Level of Significance and p-Values
     • Goodness-of-Fit Test
     • Tests of Single Coefficients
     • Tests of Linear Combinations of Coefficients
   • Additional Tests of Hypotheses in Econometric Models
     • Test of Normality of the Residuals (the Jarque-Bera Test)
     • Ramsey Regression Specification Error Test (the Ramsey RESET Test)
     • Box-Cox Test (Test of Functional Form)
     • Granger Causality Test (Test of Precedence)
     • Hausman Test (Test of Endogeneity/Exogeneity of Explanatory Variables)

        

4. Use of Indicator or Dummy Variables
   • Overview (the Representation of Qualitative Variables in Applied Econometrics)

      

5. Autocorrelation or Serial Correlation
   • Definition, Prevalence, and Consequences of Serial Correlation
     • Systematic Pattern in the Residuals
     • Positive Versus Negative Autocorrelation
   • Formal Tests of Serial Correlation
     • Durbin-Watson Test
   • Solution to the Serial Correlation Problem

      

6. Heteroscedasticity
   • Definition, Prevalence, and Consequences of Heteroscedasticity
     • Indigenous to the Use of Cross-Sectional Data
     • Examination of the Residuals--Graphical Depictions
   • Formal Tests of Heteroscedasticity
   • Solution to the Heteroscedasticity Problem
     • Application of Weighted Least Squares

        

7. Collinearity
   • Definition, Prevalence, and Consequences of Collinearity
     • Consequences for Structural Parameter Estimates and Forecasting
   • Formal Diagnostics of Collinearity

      

8. Influence Diagnostics: The Detection and Assessment of Data Outliers and Leverage Points
   • Definition and Consequences of Influence Points

      

9. Structural Change and Stability of Structural Coefficients
   • Abrupt Structural Change
     • Breakpoint(s)
     • Use of Sequential Chow Tests
   • Formal Tests of Parameter Instability
     • Recursive Estimation
     • Recursive Residuals
   • Solution(s) to Structural Change or Parameter Instability
     • Attention to the Time Frame of the Econometric Analysis
     • Use of Dummy Variables

        

10. Distributed Lag Models
    • Overview of Distributed Lag Models

       

11. ARCH and GARCH Models
    • Overview of ARCH and GARCH Models
    • Mechanics of the Autoregressive Conditional Heteroscedasticity (ARCH) Model
      • Stability Considerations
    • Mechanics of the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model

       

12. Qualitative Choice Models
    • Overview of Qualitative Choice Models
    • Binary Choice Models--The Probit Model
    • Binary Choice Models--The Logit Model
    • Estimation of Probit and Logit Models
    • Calculation of Appropriate Marginal Effects
    • The Prediction-Success Table

       

13. Censored Response Models

     

14. Pooling of Time-Series and Cross-Sectional Data
    • Overview and Examples of Pooled Time-Series and Cross-Sectional Data
    • To Pool or Not to Pool?
    • Cross-Sectionally Heteroscedastic and Time-Wise Autoregressive Models
    • Fixed and Random Effects

       

15. Simultaneous-Equation Models
    • Order Conditions for Identification
    • Impact, Interim, and Total Multipliers
16. Stability Conditions of Simultaneous-Equation Models

     

17. Seemingly Unrelated Regression (SUR) Models